Terrain Classification using Multiple Image Features

A wide variety of image processing applications require segmentation and classification ofimages. The problem becomes complex when the images are obtained in an uncontrolledenvironment with a non-uniform illumination. The selection of suitable features is a critical partof an image segmentation and classification process, where the basic objective is to identify theimage regions that are homogeneous but dissimilar to all spatially adjacent regions. This paperproposes an automatic method for the classification of a terrain using image features such asintensity, texture, and edge. The textural features are calculated using statistics of geometricalattributes of connected regions in a sequence of binary images obtained from a texture image.A pixel-wise image segmentation scheme using a multi-resolution pyramid is used to correct thesegmentation process so as to get homogeneous image regions. Localisation of texture boundariesis done using a refined-edge map obtained by convolution, thinning, thresholding, and linking.The individual regions are classified using a database generated from the features extracted fromknown samples of the actual terrain. The algorithm is used to classify airborne images of a terrainobtained from the sensor mounted on an aerial reconnaissance platform and the results arepresented

Automatic Classification of Aerial Imagery

The aerial imagery obtained from reconnaissance platform is voluminous and the defenceforces rely on image information to perform intelligent tasks. The application of a welldesigned automatic image classifier would enhance the end results of different high levelapplications thereby abridging the effort of a human analyst. Automatic image classifierscould be designed using a training data set for supervised learning or using an unsupervisedlearning. In this paper, a method, which combines both unsupervised and supervised methodsof learning is proposed

EEG Markers in Emotionally Unstable Personality Disorder-A Possible Outcome Measure for Neurofeedback: A Narrative Review.

Objectives. There is growing evidence for the use of biofeedback (BF) in affective disorders, dissocial personality disorder, and in children with histories of abuse. Electroencephalogram (EEG) markers could be used as neurofeedback in emotionally unstable personality disorder (EUPD) management especially for those at high risk of suicide when emotionally aroused. This narrative review investigates the evidence for EEG markers in EUPD. Methods. PRISMA guidelines were used to conduct a narrative review. A structured search method was developed and implemented in collaboration with an information specialist. Studies were identified via 3 electronic database searches of MEDLINE, Embase, and PsycINFO. A predesigned inclusion/exclusion criterion was applied to selected papers. A thematic analysis approach with 5 criteria was used. Results. From an initial long list of 5250 papers, 229 studies were identified and screened, of which 44 met at least 3 of the predesigned inclusion criteria. No research to date investigates EEG-based neurofeedback in EUPD. A number of different EEG biomarkers are identified but there is poor consistency between studies. Conclusions. The findings heterogeneity may be due to the disorder complexity and the variable EEG related parameters studied. An alternative explanation may be that there are a number of different neuromarkers, which could be clustered together with clinical symptomatology, to give new subdomains. Quantitative EEGs in particular may be helpful to identify more specific abnormalities. EEG standardization of neurofeedback protocols based on specific EEG abnormalities detected may facilitate targeted use of neurofeedback as an intervention in EUPD

Polycystin-1 Surface Localization Is Stimulated by Polycystin-2 and Cleavage at the G Protein-coupled Receptor Proteolytic Site

The localization of polycystin (PC)1) to the plasma membrane requires coexpression with PC2 and cleavage at the PC1 G protein-coupled receptor proteolytic site. Neither the PC1 binding capacity of PC2 nor its channel function is required for this effect

EEG Biofeedback as a Treatment for Substance Use Disorders: Review, Rating of Efficacy, and Recommendations for Further Research

Electroencephalographic (EEG) biofeedback has been employed in substance use disorder (SUD) over the last three decades. The SUD is a complex series of disorders with frequent comorbidities and EEG abnormalities of several types. EEG biofeedback has been employed in conjunction with other therapies and may be useful in enhancing certain outcomes of therapy. Based on published clinical studies and employing efficacy criteria adapted by the Association for Applied Psychophysiology and Biofeedback and the International Society for Neurofeedback and Research, alpha theta training—either alone for alcoholism or in combination with beta training for stimulant and mixed substance abuse and combined with residential treatment programs, is probably efficacious. Considerations of further research design taking these factors into account are discussed and descriptions of contemporary research are given

Terrain Classification using Multiple Image Features

A wide variety of image processing applications require segmentation and classification of images. The problem becomes complex when the images are obtained in an uncontrolled environment with a non-uniform illumination. The selection of suitable features is a critical part of an image segmentation and classification process, where the basic objective is to identify the image regions that are homogeneous but dissimilar to all spatially adjacent regions. This paper proposes an automatic method for the classification of a terrain using image features such as intensity, texture, and edge. The textural features are calculated using statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. A pixel-wise image segmentation scheme using a multi-resolution pyramid is used to correct the segmentation process so as to get homogeneous image regions. Localisation of texture boundaries is done using a refined-edge map obtained by convolution, thinning, thresholding, and linking. The individual regions are classified using a database generated from the features extracted from known samples of the actual terrain. The algorithm is used to classify airborne images of a terrain obtained from the sensor mounted on an aerial reconnaissance platform and the results are presented